A camera self-checking method for a substation scene

Abstract

The application discloses a camera self-checking method for a substation scene and relates to the technical field of substations.The method comprises the following steps: generating a camera number configuration information set and a self-checking element set according to substation camera deployment information, obtaining industry monitoring standard files and historical data through network communication technology to analyze and generate formulated camera operation requirements, generating a substation camera self-checking standard set, constructing a camera self-checking model according to historical monitoring operation data, the self-checking element set and the self-checking standard set to generate a camera self-checking report, and obtaining the fault level of the camera according to the camera self-checking report to perform fault level fusion and alarm.The application can closely combine industry standards and historical data to generate comprehensive and demand-matched self-checking elements and standard sets, the self-checking model can quickly and accurately detect the camera and accurately identify the fault level, the monitoring system is stably ensured, the safety hidden danger is reduced, the camera fault can be found and handled in time by the operation and maintenance personnel, and the operation and maintenance efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of substation technology, and in particular to a self-testing method for cameras in substation scenarios. Background Technology

[0002] As a critical node in the power system, the safe and stable operation of substations is essential for ensuring power supply. To monitor the operating status of equipment, personnel actions, and environmental conditions within substations in real time, cameras are widely used in various areas. These cameras provide intuitive video images, helping maintenance personnel to promptly identify potential safety hazards and faults. However, substation cameras currently face numerous challenges in actual operation. On the one hand, the complex substation environment, with its high temperatures, high humidity, and strong electromagnetic interference, easily leads to camera malfunctions such as blurred lenses, image distortion, and signal interruptions. These malfunctions not only affect the normal monitoring function of the cameras but may also prevent maintenance personnel from obtaining accurate information in a timely manner, thus delaying fault handling and posing a serious threat to the safe operation of the substation. On the other hand, existing camera self-testing methods are mostly unspecific and do not fully consider the unique characteristics of substation scenarios. Traditional self-testing methods mainly focus on basic camera functions, such as image clarity and color reproduction, but lack effective testing methods for functions specific to substation scenarios, such as temperature monitoring of high-voltage equipment and intelligent identification of personnel violations. Furthermore, existing self-testing methods often fail to accurately assess the fault level of cameras, resulting in the inability to take timely and appropriate alarm measures when faults occur, thus affecting the efficiency of fault handling. Therefore, in order to improve the reliability and stability of substation cameras and ensure their normal operation in complex substation environments, there is an urgent need for a camera self-testing method for substation scenarios that can comprehensively and accurately detect camera faults and take timely and effective alarm measures based on the fault level. Summary of the Invention

[0003] This invention provides a self-testing method for cameras in substation scenarios, comprising:

[0004] Step S1: The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number of cameras deployed and the deployment area of ​​the substation based on the camera configuration information, names the camera deployment area with area code, and numbers the cameras based on the number of cameras deployed and the area code of the substation, generating a camera number configuration information set.

[0005] Step S2: The self-inspection center extracts the self-inspection elements of the substation cameras according to the camera number configuration information set, generates the substation camera self-inspection element set, obtains the substation industry monitoring standard documents and the historical monitoring operation data of the substation cameras through network communication technology, analyzes them to generate customized substation camera operation requirements, and generates a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set.

[0006] Step S3: The self-inspection center generates a basic self-inspection classification dataset and a camera-specific scene function self-inspection classification dataset based on the historical monitoring operation data of the substation cameras, the substation camera self-inspection element set, and the substation camera self-inspection standard set. It then constructs a basic self-inspection sub-model based on the basic self-inspection classification dataset and a camera-specific scene function self-inspection sub-model based on the camera-specific scene function self-inspection classification dataset. Based on these datasets, it generates a basic self-inspection fault combination dataset and a camera-specific scene function self-inspection fault combination dataset. A camera fault level information dataset is generated using a camera fault level generation algorithm. A camera fault level identification sub-model is constructed based on this dataset. Finally, a camera self-inspection model is built based on the basic self-inspection sub-model, the camera-specific scene function self-inspection sub-model, and the camera fault level identification sub-model.

[0007] Step S4: The self-inspection center collects camera self-inspection data information at regular intervals through the built-in information acquisition sensor in the camera based on the self-inspection element set of the substation camera and inputs it into the camera self-inspection model to generate a camera self-inspection report.

[0008] Step S5: The self-inspection center obtains the fault level of the camera based on the camera self-inspection report, obtains the substation camera fusion fault level through the fault level fusion algorithm, and selects an alarm method to trigger an alarm.

[0009] The above-described camera self-testing method for substation scenarios includes the following sub-steps: The self-testing center obtains the substation camera configuration information based on the substation camera deployment information; it then obtains the number and deployment area of ​​the cameras based on the configuration information and assigns area codes to the camera deployment areas; finally, it assigns numbers to the cameras based on the number and area codes, generating a camera number configuration information set.

[0010] Step S11: The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information and generates a camera configuration information set.

[0011] Step S12: The self-inspection center obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information set, assigns numbers to the cameras, and generates a camera number configuration information set.

[0012] The above-described camera self-inspection method for substation scenarios includes the following substation camera self-inspection element set: The self-inspection center extracts substation camera self-inspection elements based on camera number configuration information set, generates a substation camera self-inspection element set, and analyzes substation industry monitoring standard documents and historical monitoring operation data of substation cameras through network communication technology to generate customized substation camera operation requirements. The generation of a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set includes the following sub-steps:

[0013] Step S21: The self-inspection center generates a substation camera self-inspection element set based on the camera number configuration information set;

[0014] Step S22: The self-inspection center uses network communication technology to obtain substation industry monitoring standard documents and historical monitoring operation data of substation cameras, analyzes them, and generates customized substation camera operation requirements.

[0015] Step S23: The self-inspection center generates a set of self-inspection standards for substation cameras based on the established substation camera operation requirements and the set of self-inspection elements for substation cameras.

[0016] The above-described camera self-inspection method for substation scenarios includes the following sub-steps: A self-inspection center generates a basic camera self-inspection dataset and a scene-specific functional self-inspection dataset based on historical monitoring data of substation cameras, a set of substation camera self-inspection elements, and a set of substation camera self-inspection standards. Based on the basic camera self-inspection dataset, a basic camera self-inspection sub-model is constructed. Based on the scene-specific functional self-inspection dataset, a scene-specific functional self-inspection sub-model is constructed. Based on the basic camera self-inspection dataset and the scene-specific functional self-inspection dataset, a basic camera self-inspection fault combination dataset and a scene-specific functional self-inspection fault combination dataset are generated. A camera fault level information dataset is generated using a camera fault level generation algorithm. Based on the camera fault level information dataset, a camera fault level identification sub-model is constructed. The construction of a camera self-inspection model based on the basic camera self-inspection sub-model, the scene-specific functional self-inspection sub-model, and the camera fault level identification sub-model includes the following sub-steps:

[0017] Step S31: The self-inspection center constructs a basic self-inspection sub-model for the camera based on the historical monitoring and operation data of the substation camera, the self-inspection element set of the substation camera, and the self-inspection standard set of the substation camera.

[0018] Step S32: The self-inspection center constructs a self-inspection sub-model for specific scene functions of the camera based on the historical monitoring and operation data of the substation camera, the self-inspection element set of the substation camera, and the self-inspection standard set of the substation camera.

[0019] Step S33: The self-inspection center generates a camera fault level information dataset based on the camera basic self-inspection division dataset and the camera specific scene function self-inspection division dataset through the camera fault level generation algorithm, and constructs a camera fault level identification sub-model based on the camera fault level information dataset.

[0020] Step S34: The self-test center integrates the camera basic self-test sub-model, the camera specific scene function self-test sub-model, and the fault level identification sub-model to construct the camera self-test model.

[0021] The above-described camera self-testing method for substation scenarios, wherein the self-testing center periodically collects camera self-testing data information through the built-in information acquisition sensor in the camera based on the substation camera self-testing element set and inputs it into the camera self-testing model to generate a camera self-testing report, includes the following sub-steps:

[0022] Step S41: The self-inspection center generates a camera self-inspection information dataset by periodically collecting camera self-inspection data information through the built-in information acquisition sensor in the camera based on the self-inspection element set of the substation camera.

[0023] Step S42: The self-test center inputs the camera self-test dataset into the camera self-test model for data analysis and generates a camera self-test report.

[0024] The above-described camera self-testing method for substation scenarios includes the following sub-steps: The self-testing center obtains the camera's fault level based on the self-test report, uses a fault level fusion algorithm to obtain the substation camera's fused fault level, and selects an alarm method to trigger an alarm.

[0025] Step S51: The self-inspection center obtains the fault level of the camera based on the camera self-inspection report and obtains the substation camera fusion fault level through the fault level fusion algorithm;

[0026] Step S52: The self-test center selects the alarm method and issues an alarm based on the fault level of the substation camera fusion.

[0027] This invention also provides a camera self-testing system for substation scenarios, comprising:

[0028] The camera configuration information acquisition module obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number of cameras deployed and the deployment area of ​​the substation based on the camera configuration information, names the camera deployment area with area code, and assigns a number to the camera based on the number of cameras deployed and the area code of the substation, generating a set of camera number configuration information.

[0029] The camera self-inspection element and self-inspection standard generation module extracts substation camera self-inspection elements based on the camera number configuration information set, generates a substation camera self-inspection element set, obtains substation industry monitoring standard documents and historical monitoring operation data of substation cameras through network communication technology, analyzes and generates customized substation camera operation requirements, and generates a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set.

[0030] The camera self-inspection model construction module generates a basic self-inspection classification dataset and a camera-specific scene function self-inspection classification dataset based on historical monitoring and operation data of substation cameras, substation camera self-inspection element set, and substation camera self-inspection standard set. It then constructs a basic self-inspection sub-model based on the basic self-inspection classification dataset, a camera-specific scene function self-inspection sub-model based on the camera-specific scene function self-inspection classification dataset, and generates a basic self-inspection fault combination dataset and a camera-specific scene function self-inspection fault combination dataset based on the basic and specific self-inspection classification datasets. A camera fault level information dataset is generated through a camera fault level generation algorithm. A camera fault level identification sub-model is constructed based on the camera fault level information dataset. Finally, a camera self-inspection model is built based on the basic self-inspection sub-model, the camera-specific scene function self-inspection sub-model, and the camera fault level identification sub-model.

[0031] The camera self-test report acquisition module collects camera self-test data information at regular intervals through the built-in information acquisition sensor in the camera based on the substation camera self-test element set and inputs it into the camera self-test model to generate a camera self-test report.

[0032] The camera fault alarm module obtains the fault level of the camera based on the camera self-test report, obtains the substation camera fusion fault level through a fault level fusion algorithm, and selects an alarm method to trigger an alarm.

[0033] The camera self-testing system for substation scenarios described above includes, in particular, a camera configuration information acquisition and numbering module, comprising:

[0034] The camera configuration information acquisition submodule acquires the camera configuration information of the substation based on the substation camera deployment information and generates a camera configuration information set.

[0035] The camera numbering submodule obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information set, assigns numbers to the cameras, and generates a camera numbering configuration information set.

[0036] As described above, a camera self-inspection system for substation scenarios includes a camera self-inspection element self-inspection standard generation module, which specifically comprises:

[0037] The camera self-inspection element acquisition submodule generates a substation camera self-inspection element set based on the camera number and configuration information set.

[0038] The camera operation requirements acquisition submodule uses network communication technology to acquire industry monitoring standard documents for substations and historical monitoring operation data of substation cameras, analyzes them, and generates customized substation camera operation requirements.

[0039] The camera self-inspection standard generation submodule generates a set of self-inspection standards for substation cameras based on the established substation camera operation requirements and the set of self-inspection elements for substation cameras.

[0040] The camera self-testing system for substation scenarios described above includes, in particular, a camera self-testing model construction module comprising:

[0041] The camera basic self-inspection sub-module constructs a camera basic self-inspection sub-model based on the historical monitoring and operation data of substation cameras, the substation camera self-inspection element set, and the substation camera self-inspection standard set.

[0042] The sub-module for constructing a self-test sub-model for camera specific scene functions constructs a self-test sub-model for camera specific scene functions based on the historical monitoring and operation data of substation cameras, the self-test element set of substation cameras, and the self-test standard set of substation cameras.

[0043] The camera fault level identification sub-model construction module generates a camera fault level information dataset based on the camera basic self-inspection dataset and the camera specific scene function self-inspection dataset, and constructs a camera fault level identification sub-model based on the camera fault level information dataset.

[0044] The sub-model integration sub-module integrates the camera basic self-test sub-model, the camera specific scene function self-test sub-model, and the fault level identification sub-model to build a camera self-test model.

[0045] The camera self-testing system for substation scenarios described above includes, in particular, a camera self-testing report acquisition module comprising:

[0046] The camera self-test information acquisition submodule generates a camera self-test information dataset by periodically collecting camera self-test data information through the built-in information acquisition sensor in the camera based on the substation camera self-test element set.

[0047] The camera self-test report generation submodule takes the camera self-test dataset and inputs it into the camera self-test model for data analysis to generate a camera self-test report.

[0048] As described above, a camera self-testing system for substation scenarios includes a camera fault alarm module, specifically comprising:

[0049] The substation camera fault level fusion submodule obtains the fault level of the camera based on the camera self-test report and obtains the fused fault level of the substation camera through a fault level fusion algorithm.

[0050] The fault alarm submodule selects the alarm method based on the fault level fused from the substation cameras.

[0051] The beneficial effects of this invention are as follows: This invention can closely integrate industry standards and historical monitoring data of substations to generate a comprehensive set of camera self-inspection elements and standards that meets the monitoring needs of substations, and construct a camera self-inspection model. This self-inspection model can quickly and accurately perform basic and scenario-specific functional tests on cameras, while accurately identifying camera fault levels and generating camera self-inspection reports. Furthermore, this invention effectively ensures the stable operation of the monitoring system, provides solid support for the safe operation of substations, and greatly reduces safety hazards. Through precise fault location and alarms, maintenance personnel can promptly detect and handle camera faults within the substation, thereby improving maintenance efficiency and effectively reducing maintenance costs. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0053] Figure 1 This is a flowchart of a camera self-testing method for substation scenarios provided in Embodiment 1 of this application;

[0054] Figure 2 This is a schematic diagram of a camera self-testing system for substation scenarios provided in Embodiment 2 of this application. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1

[0057] like Figure 1 As shown, Embodiment 1 of this application provides a camera self-testing method for substation scenarios, which includes the following steps:

[0058] Step S1: The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number of cameras deployed and the deployment area of ​​the substation based on the camera configuration information, names the camera deployment area with area code, and numbers the cameras based on the number of cameras deployed and the area code of the substation, generating a camera number configuration information set.

[0059] Furthermore, the self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information, names the camera deployment area with area code, and numbers the cameras according to the number of cameras deployed in the substation and the area code, generating a camera number configuration information set including the following sub-steps:

[0060] Step S11: The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information and generates a camera configuration information set.

[0061] Specifically, the substation camera configuration information set includes, but is not limited to, camera technical parameters, camera functions, camera layout, and camera field of view.

[0062] Step S12: The self-inspection center obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information set, assigns numbers to the cameras, and generates a camera number configuration information set.

[0063] Specifically, the camera configuration information set is analyzed to obtain the camera deployment areas and the number of cameras in different areas within the substation. The total number of cameras in the substation is then calculated by summing the numbers from different areas. Each deployment area is assigned a region code, and the cameras are numbered accordingly. For example, substation cameras might be deployed in areas such as the main transformer area, distribution room area, high-voltage switchgear area, and substation entrance / exit area. These areas would be named with region codes such as BQ, PD, GY, and CR. If five cameras are deployed in the main transformer area, they would be numbered sequentially as "BQ-001", "BQ-002", "BQ-003", "BQ-004", and "BQ-005". This camera numbering information is then combined with the camera configuration information set to generate a camera number configuration information set.

[0064] Step S2: The self-inspection center extracts the self-inspection elements of the substation cameras according to the camera number configuration information set, generates the substation camera self-inspection element set, obtains the substation industry monitoring standard documents and the historical monitoring operation data of the substation cameras through network communication technology, analyzes them to generate customized substation camera operation requirements, and generates a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set.

[0065] Furthermore, the self-inspection center extracts self-inspection elements from the substation cameras based on the camera number configuration information set, generates a substation camera self-inspection element set, and obtains substation industry monitoring standard documents and historical monitoring operation data of substation cameras through network communication technology for analysis to generate customized substation camera operation requirements. The generation of a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set includes the following sub-steps:

[0066] Step S21: The self-inspection center generates a substation camera self-inspection element set based on the camera number configuration information set;

[0067] Specifically, the monitoring content and functions of each camera are obtained based on the camera number and configuration information. Self-inspection elements for each camera are then generated based on these elements, and a set of self-inspection elements for substation cameras is constructed. For example, if the monitoring content of a substation entrance / exit camera is the monitoring of personnel and vehicles within the substation entrance / exit area, and its function is security and early warning, triggering an alarm upon detecting abnormal personnel or vehicles entering or exiting, then its self-inspection elements would be the accuracy of identifying personnel and vehicles entering and exiting, and the rate of transmitting abnormal information to trigger an alarm.

[0068] Step S22: The self-inspection center uses network communication technology to obtain substation industry monitoring standard documents and historical monitoring operation data of substation cameras, analyzes them, and generates customized substation camera operation requirements.

[0069] Specifically, based on the industry monitoring standard documents for substations, we obtain the industry monitoring standards for different areas within the substation. Based on the historical monitoring operation data of the substation cameras, we obtain the historical monitoring content, data storage and transmission, and normal operation data of the cameras working in conjunction with other equipment. Based on the industry monitoring standards for different areas within the substation and the normal operation data of the cameras, we generate and formulate standardized substation camera operation requirements.

[0070] Step S23: The self-inspection center generates a self-inspection standard set for substation cameras based on the established substation camera operation requirements and the substation camera self-inspection element set;

[0071] Specifically, based on the established substation camera operation requirements, normal operation data information of each camera is obtained. Based on the substation camera self-inspection element set and the normal operation data information of each camera, self-inspection standard values ​​of each camera self-inspection element are set. Based on the self-inspection standard values ​​of each camera self-inspection element, a substation camera self-inspection standard set is constructed.

[0072] Step S3: The self-inspection center generates a basic self-inspection classification dataset and a camera-specific scene function self-inspection classification dataset based on the historical monitoring operation data of the substation cameras, the substation camera self-inspection element set, and the substation camera self-inspection standard set. It then constructs a basic self-inspection sub-model based on the basic self-inspection classification dataset and a camera-specific scene function self-inspection sub-model based on the camera-specific scene function self-inspection classification dataset. Based on these datasets, it generates a basic self-inspection fault combination dataset and a camera-specific scene function self-inspection fault combination dataset. A camera fault level information dataset is generated using a camera fault level generation algorithm. A camera fault level identification sub-model is constructed based on this dataset. Finally, a camera self-inspection model is built based on the basic self-inspection sub-model, the camera-specific scene function self-inspection sub-model, and the camera fault level identification sub-model.

[0073] Furthermore, the self-inspection center generates a basic self-inspection classification dataset and a scene-specific functional self-inspection classification dataset for cameras based on historical monitoring and operation data of substation cameras, the self-inspection element set of substation cameras, and the self-inspection standard set of substation cameras. It then constructs a basic self-inspection sub-model based on the basic self-inspection classification dataset and a scene-specific functional self-inspection sub-model based on the scene-specific functional self-inspection classification dataset. Finally, it generates a basic self-inspection fault combination dataset and a scene-specific functional self-inspection fault combination dataset based on the basic and scene-specific self-inspection classification datasets. A camera fault level information dataset is generated using a camera fault level generation algorithm. A camera fault level identification sub-model is then constructed based on this dataset. The construction of the camera self-inspection model, based on the basic self-inspection sub-model, scene-specific functional self-inspection sub-model, and fault level identification sub-model, includes the following sub-steps:

[0074] Step S31: The self-inspection center constructs a basic self-inspection sub-model for the camera based on the historical monitoring and operation data of the substation camera, the self-inspection element set of the substation camera, and the self-inspection standard set of the substation camera.

[0075] Specifically, based on the historical monitoring and operation data of the substation cameras, the historical monitoring and operation data corresponding to the substation camera self-inspection element set is obtained. Basic camera operation data is extracted from the historical monitoring and operation data, and a basic camera self-inspection operation dataset is constructed. Basic camera self-inspection elements are extracted from the substation camera self-inspection element set, and a basic camera self-inspection element set is constructed. Basic camera self-inspection standards are extracted from the substation camera self-inspection standard set, and a basic camera self-inspection standard set is constructed. Finally, the basic camera self-inspection operation dataset, the basic camera self-inspection element set, and the basic camera self-inspection standard set are used to apply the basic camera self-inspection formula. The camera basic self-test operation dataset is divided into a normal operation subset and a fault subset. Here, JCZ represents the camera basic self-test subset, R represents the number of cameras in the substation, Q represents the number of camera basic self-test elements, FHS represents the normal operation subset, BFS represents the fault subset, and ys... rq bz is the operational data of the q-th basic self-test element of the r-th camera. rq For the r-th camera, the q-th basic self-test standard, pc rq Let q be the deviable value of the basic self-inspection standard for the r-th camera. Based on the camera basic self-inspection dataset JCZ and the camera basic self-inspection element set, a camera basic self-inspection sub-model is constructed using machine learning techniques.

[0076] Step S32: The self-inspection center constructs a self-inspection sub-model for specific scene functions of the camera based on the historical monitoring and operation data of the substation camera, the self-inspection element set of the substation camera, and the self-inspection standard set of the substation camera.

[0077] Specifically, based on historical monitoring data of the cameras, specific scene function operation data of each camera is extracted and a self-inspection operation dataset for specific scene functions is constructed. Based on the substation camera self-inspection element set, specific scene function self-inspection elements of each camera are extracted and a self-inspection element set for specific scene functions is constructed. Based on the substation camera self-inspection standard set, specific scene function self-inspection standards of each camera are extracted and a self-inspection standard set for specific scene functions is constructed. Finally, based on the camera specific scene function self-inspection operation dataset, the camera specific scene function self-inspection element set, and the camera specific scene function self-inspection standard set, a self-inspection formula for specific scene functions is used. The camera-specific scene function self-test operation dataset is divided into a subset of normal operation data and a subset of fault data for each camera's specific scene function self-test. Here, TCG represents the camera-specific scene function self-test dataset, n is the number of cameras, im is the number of specific scene function self-test elements for the i-th camera, and TCH... i For the i-th camera, the specific scene function self-test normal operation data subset, TCF i For the i-th camera, yx is a subset of the self-test fault data for a specific scene function. ij For the operational data of the j-th specific scene function self-test element of the i-th camera, tz ij For the j-th specific scene function self-test standard of the i-th camera, tp ij Let be the deviable value of the self-test standard for the j-th specific scene function of the i-th camera. Based on the self-test data set TCG and the self-test feature set for the specific scene function of the camera, a self-test sub-model for the specific scene function of the camera is constructed using machine learning techniques.

[0078] Step S33: The self-inspection center generates a camera fault level information dataset based on the camera basic self-inspection division dataset and the camera specific scene function self-inspection division dataset through the camera fault level generation algorithm, and constructs a camera fault level identification sub-model based on the camera fault level information dataset.

[0079] Specifically, based on the camera basic self-test partition dataset JCZ, a subset of camera basic self-test fault data is obtained by BFS and represented as BFS = {jg1,jg2,…,jg...}. g ,…,jg G}, where BFS is a subset of the camera's basic self-test fault data, jg gLet g be the g-th basic self-test fault data, and G be the number of basic self-test fault data. A combined dataset of camera basic self-test faults is constructed using BFS based on a subset of the camera basic self-test fault data. Where JZH is the dataset of basic self-test fault combinations for cameras, T is the number of basic self-test fault combinations for cameras, and jg tf Let tF be the basic self-test fault data of the f-th camera in the basic self-test fault combination of the t-th camera, and tF be the number of basic self-test fault data of the cameras in the basic self-test fault combination of the t-th camera.

[0080] Based on the self-test data of camera functions in specific scenes, the dataset TCG was divided into subsets of fault data for each camera's self-test function in specific scenes, and the TCF was obtained. i And represented as TCF i ={gs i1 ,gs i2 ,…,gs is ,…,gs iS}, where TCF i For the subset of self-test fault data of the i-th camera in a specific scene, gs is Let $S$ be the scene-specific self-test fault data for the $i$-th camera and $s$ be the number of scene-specific self-test fault data for the $i$-th camera. The fault data is calculated based on the subset $TCF$ of scene-specific self-test fault data for each camera. i Construct a camera scene-specific self-test fault dataset TGJ = {TCF1, TCF2, ..., TCF} i ,…,TCF n}, where TGJ is a self-test fault dataset for camera functions in a specific scene, and TCF is... i Given a subset of scene-specific function self-test fault data for the i-th camera, where n is the number of cameras, construct a combined scene-specific function self-test fault dataset based on the TGJ dataset.

[0081] Where TZH is the dataset of camera-specific scene function self-test fault combinations, n is the number of cameras in the substation, iC is the number of camera-specific scene function self-test fault combinations for the i-th camera, icY is the number of specific scene function self-test fault data for the c-th camera-specific scene function self-test fault combination of the i-th camera, and gs icy The self-test fault data for the y-th specific scene function of the c-th camera is a combination of self-test faults for the c-th camera in a specific scene function.

[0082] Based on the camera basic self-test fault combination dataset JZH and the camera specific scene function self-test fault combination dataset TZH, a camera fault level generation algorithm formula is used. Obtain the fault level of different fault combinations for each camera, where GD itc The fault level of the i-th camera is determined by combining the basic self-test fault combination of the t-th camera with the scene-specific function self-test fault combination of the c-th camera of the i-th camera. α1 is the weighting coefficient of the basic self-test faults of the cameras, tF is the number of basic self-test fault data in the basic self-test fault combination of the t-th camera, and β is the fault level of the t-th camera. tf Let ys be the influence weighting coefficient of the f-th basic self-test fault in the t-th basic self-test fault combination of the camera. tf Let jg be the impact value of the f-th basic self-test fault in the t-th camera basic self-test fault combination. tf Let f be the basic self-test fault data of the t-th camera basic self-test fault combination, α2 be the weight coefficient of the camera specific scene function self-test fault, icY be the number of specific scene function self-test fault data of the c-th camera specific scene function self-test fault combination, and δ be the number of specific scene function self-test fault data of the i-th camera. icy tg represents the influence weighting coefficient of the y-th scene-specific function self-test fault in the combination of scene-specific function self-test faults of the c-th camera and the i-th camera. icy Let gs be the impact value of the y-th scene-specific function self-test fault in the combination of scene-specific function self-test faults of the c-th camera and the i-th camera. icy This refers to the y-th scene-specific function self-test fault data of the c-th camera's scene-specific function self-test fault combination for the i-th camera. The fault level GD of the i-th camera is determined by combining the basic self-test fault combination of the t-th camera with the c-th camera's scene-specific function self-test fault combination. itc A camera fault level information dataset is constructed. Based on the camera basic self-test fault data subset BFS, the camera scene-specific functional self-test fault dataset TGJ, and the camera fault level information dataset, a camera fault level identification sub-model is built using machine learning techniques.

[0083] Step S34: The self-test center integrates the camera basic self-test sub-model, the camera specific scene function self-test sub-model, and the fault level identification sub-model to construct the camera self-test model.

[0084] Specifically, a camera self-test model is constructed by integrating the basic self-test sub-model of the camera, the self-test sub-model of the camera's specific scene functions, and the fault level identification sub-model through model fusion technology.

[0085] Step S4: The self-inspection center collects camera self-inspection data information at regular intervals through the built-in information acquisition sensor in the camera based on the self-inspection element set of the substation camera and inputs it into the camera self-inspection model to generate a camera self-inspection report.

[0086] Furthermore, the self-inspection center, based on the substation camera self-inspection element set, periodically collects camera self-inspection data through the built-in information acquisition sensors in the cameras and inputs it into the camera self-inspection model to generate a camera self-inspection report, including the following sub-steps:

[0087] Step S41: The self-inspection center generates a camera self-inspection information dataset by periodically collecting camera self-inspection data information through the built-in information acquisition sensor in the camera based on the self-inspection element set of the substation camera.

[0088] Specifically, the substation's operation and maintenance personnel set the camera's self-test time according to the self-test requirements, and periodically collect the camera's self-test information data through the built-in information acquisition sensor in the camera based on the camera's self-test time and the substation's camera self-test element set.

[0089] Step S42: The self-test center inputs the camera self-test dataset into the camera self-test model for data analysis and generates a camera self-test report;

[0090] Specifically, the camera self-inspection report includes, but is not limited to, the number of each camera in the substation, the basic self-inspection qualified elements and their self-inspection data information, the specific scenario function self-inspection qualified elements and their self-inspection data information, the basic self-inspection fault elements and their fault data information, the specific scenario function self-inspection fault elements and their fault data information, and the fault level.

[0091] Step S5: The self-inspection center obtains the fault level of the camera based on the camera self-inspection report, obtains the substation camera fusion fault level through the fault level fusion algorithm, and selects an alarm method to trigger an alarm.

[0092] Furthermore, the self-inspection center obtains the fault level of the camera based on the camera's self-inspection report, obtains the substation camera fusion fault level through a fault level fusion algorithm, and selects an alarm method to trigger an alarm, including the following sub-steps:

[0093] Step S51: The self-inspection center obtains the fault level of the camera based on the camera self-inspection report and obtains the substation camera fusion fault level through the fault level fusion algorithm;

[0094] Specifically, through fault level fusion algorithm Obtain the fault level of the substation camera fusion, where GRH is the fault level of the substation camera fusion, n is the number of faulty cameras in the substation, and λ is the number of faulty cameras. i Let GD be the fault impact weighting coefficient for the i-th faulty camera. i Let be the fault level of the i-th faulty camera.

[0095] Step S52: The self-test center selects the alarm method and issues an alarm based on the fault level of the substation camera fusion.

[0096] Specifically, the alarm methods in the substation include, but are not limited to, telephone alarm, SMS alarm, email alarm, indicator light alarm, and voice alarm. The alarm method that matches the fault level of the substation camera is selected based on the fault level of the substation camera, so as to accurately notify the corresponding maintenance personnel to repair the camera.

[0097] Example 2

[0098] like Figure 2 As shown, Embodiment 2 of this application provides a camera self-testing system for substation scenarios, including:

[0099] The camera configuration information acquisition module 21 acquires the camera configuration information of the substation based on the substation camera deployment information, acquires the number of cameras deployed and the deployment area of ​​the substation based on the camera configuration information, names the camera deployment area with area code, and assigns a number to the camera based on the number of cameras deployed and the area code of the substation, generating a camera number configuration information set.

[0100] Furthermore, the camera configuration information acquisition module 21 includes the following sub-modules:

[0101] The camera configuration information acquisition submodule acquires the camera configuration information of the substation based on the substation camera deployment information and generates a camera configuration information set.

[0102] Specifically, the substation camera configuration information set includes, but is not limited to, camera technical parameters, camera functions, camera layout, and camera field of view.

[0103] The camera numbering submodule obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information set, and numbers the cameras to generate a camera numbering configuration information set.

[0104] Specifically, the camera configuration information set is analyzed to obtain the camera deployment areas and the number of cameras in different areas within the substation. The total number of cameras in the substation is then calculated by summing the numbers from different areas. Each deployment area is assigned a region code, and the cameras are numbered accordingly. For example, substation cameras might be deployed in areas such as the main transformer area, distribution room area, high-voltage switchgear area, and substation entrance / exit area. These areas would be named with region codes such as BQ, PD, GY, and CR. If five cameras are deployed in the main transformer area, they would be numbered sequentially as "BQ-001", "BQ-002", "BQ-003", "BQ-004", and "BQ-005". This camera numbering information is then combined with the camera configuration information set to generate a camera number configuration information set.

[0105] The camera self-inspection element and self-inspection standard generation module 22 extracts the substation camera self-inspection elements according to the camera number configuration information set, generates the substation camera self-inspection element set, obtains the substation industry monitoring standard document and the historical monitoring operation data of the substation camera through network communication technology, analyzes and generates the customized substation camera operation requirements, and generates the substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set.

[0106] Furthermore, the camera self-inspection element self-inspection standard generation module 22 includes the following sub-modules:

[0107] The camera self-inspection element acquisition submodule generates a substation camera self-inspection element set based on the camera number and configuration information set.

[0108] Specifically, the monitoring content and functions of each camera are obtained based on the camera number and configuration information. Self-inspection elements for each camera are then generated based on these elements, and a set of self-inspection elements for substation cameras is constructed. For example, if the monitoring content of a substation entrance / exit camera is the monitoring of personnel and vehicles within the substation entrance / exit area, and its function is security and early warning, triggering an alarm upon detecting abnormal personnel or vehicles entering or exiting, then its self-inspection elements would be the accuracy of identifying personnel and vehicles entering and exiting, and the rate of transmitting abnormal information to trigger an alarm.

[0109] The camera operation requirements acquisition submodule uses network communication technology to acquire industry monitoring standard documents for substations and historical monitoring operation data of substation cameras, analyzes them, and generates customized substation camera operation requirements.

[0110] Specifically, based on the industry monitoring standard documents for substations, we obtain the industry monitoring standards for different areas within the substation. Based on the historical monitoring operation data of the substation cameras, we obtain the historical monitoring content, data storage and transmission, and normal operation data of the cameras working in conjunction with other equipment. Based on the industry monitoring standards for different areas within the substation and the normal operation data of the cameras, we generate and formulate standardized substation camera operation requirements.

[0111] The camera self-inspection standard generation submodule generates a set of substation camera self-inspection standards based on the established substation camera operation requirements and substation camera self-inspection element set.

[0112] Specifically, based on the established substation camera operation requirements, normal operation data information of each camera is obtained. Based on the substation camera self-inspection element set and the normal operation data information of each camera, self-inspection standard values ​​of each camera self-inspection element are set. Based on the self-inspection standard values ​​of each camera self-inspection element, a substation camera self-inspection standard set is constructed.

[0113] The camera self-inspection model construction module 23 generates a basic self-inspection classification dataset and a camera-specific scene function self-inspection classification dataset based on the historical monitoring and operation data of substation cameras, the substation camera self-inspection element set, and the substation camera self-inspection standard set. It then constructs a basic self-inspection sub-model based on the basic self-inspection classification dataset and a camera-specific scene function self-inspection sub-model based on the camera-specific scene function self-inspection classification dataset. It also generates a basic self-inspection fault combination dataset and a camera-specific scene function self-inspection fault combination dataset based on the basic self-inspection classification dataset and the camera-specific scene function self-inspection classification dataset. Through a camera fault level generation algorithm, it generates a camera fault level information dataset and constructs a camera fault level identification sub-model based on the camera fault level information dataset. Finally, it constructs a camera self-inspection model based on the basic self-inspection sub-model, the camera-specific scene function self-inspection sub-model, and the camera fault level identification sub-model.

[0114] Furthermore, the camera self-test model construction module 23 includes the following sub-modules:

[0115] The camera basic self-inspection sub-module constructs a camera basic self-inspection sub-model based on the historical monitoring and operation data of substation cameras, the substation camera self-inspection element set, and the substation camera self-inspection standard set.

[0116] Specifically, based on the historical monitoring and operation data of the substation cameras, the historical monitoring and operation data corresponding to the substation camera self-inspection element set is obtained. Basic camera operation data is extracted from the historical monitoring and operation data, and a basic camera self-inspection operation dataset is constructed. Basic camera self-inspection elements are extracted from the substation camera self-inspection element set, and a basic camera self-inspection element set is constructed. Basic camera self-inspection standards are extracted from the substation camera self-inspection standard set, and a basic camera self-inspection standard set is constructed. Finally, the basic camera self-inspection operation dataset, the basic camera self-inspection element set, and the basic camera self-inspection standard set are used to apply the basic camera self-inspection formula. The camera basic self-test operation dataset is divided into a normal operation subset and a fault subset. Here, JCZ represents the camera basic self-test subset, R represents the number of cameras in the substation, Q represents the number of camera basic self-test elements, FHS represents the normal operation subset, BFS represents the fault subset, and ys... rq bz is the operational data of the q-th basic self-test element of the r-th camera. rq For the r-th camera, the q-th basic self-test standard, pc rq Let q be the deviable value of the basic self-inspection standard for the r-th camera. Based on the camera basic self-inspection dataset JCZ and the camera basic self-inspection element set, a camera basic self-inspection sub-model is constructed using machine learning techniques.

[0117] The sub-module for constructing a self-test sub-model for camera specific scene functions constructs a self-test sub-model for camera specific scene functions based on the historical monitoring and operation data of substation cameras, the self-test element set of substation cameras, and the self-test standard set of substation cameras.

[0118] Specifically, based on historical monitoring data of the cameras, specific scene function operation data of each camera is extracted and a self-inspection operation dataset for specific scene functions is constructed. Based on the substation camera self-inspection element set, specific scene function self-inspection elements of each camera are extracted and a self-inspection element set for specific scene functions is constructed. Based on the substation camera self-inspection standard set, specific scene function self-inspection standards of each camera are extracted and a self-inspection standard set for specific scene functions is constructed. Finally, based on the camera specific scene function self-inspection operation dataset, the camera specific scene function self-inspection element set, and the camera specific scene function self-inspection standard set, a self-inspection formula for specific scene functions is used. The camera-specific scene function self-test operation dataset is divided into a subset of normal operation data and a subset of fault data for each camera's specific scene function self-test. Here, TCG represents the camera-specific scene function self-test dataset, n is the number of cameras, im is the number of specific scene function self-test elements for the i-th camera, and TCH...i For the i-th camera, the specific scene function self-test normal operation data subset, TCF i For the i-th camera, yx is a subset of the self-test fault data for a specific scene function. ij For the operational data of the j-th specific scene function self-test element of the i-th camera, tz ij For the j-th specific scene function self-test standard of the i-th camera, tp ij Let be the deviable value of the self-test standard for the j-th specific scene function of the i-th camera. Based on the self-test data set TCG and the self-test feature set for the specific scene function of the camera, a self-test sub-model for the specific scene function of the camera is constructed using machine learning techniques.

[0119] The camera fault level identification sub-model construction module generates a camera fault level information dataset based on the camera basic self-inspection dataset and the camera specific scene function self-inspection dataset, and constructs a camera fault level identification sub-model based on the camera fault level information dataset.

[0120] Specifically, based on the camera basic self-test partition dataset JCZ, a subset of camera basic self-test fault data is obtained by BFS and represented as BFS = {jg1,jg2,…,jg...}. g ,…,jg G}, where BFS is a subset of the camera's basic self-test fault data, jg g Let g be the g-th basic self-test fault data, and G be the number of basic self-test fault data. A combined dataset of camera basic self-test faults is constructed using BFS based on a subset of the camera basic self-test fault data. Where JZH is the dataset of basic self-test fault combinations for cameras, T is the number of basic self-test fault combinations for cameras, and jg tf Let tF be the basic self-test fault data of the f-th camera in the basic self-test fault combination of the t-th camera, and tF be the number of basic self-test fault data of the cameras in the basic self-test fault combination of the t-th camera.

[0121] Based on the self-test data of camera functions in specific scenes, the dataset TCG was divided into subsets of fault data for each camera's self-test function in specific scenes, and the TCF was obtained. i And represented as TCF i ={gs i1 ,gs i2 ,…,gs is ,…,gs iS}, where TCF i For the subset of self-test fault data of the i-th camera in a specific scene, gs isLet $S$ be the scene-specific self-test fault data for the $i$-th camera and $s$ be the number of scene-specific self-test fault data for the $i$-th camera. The fault data is calculated based on the subset $TCF$ of scene-specific self-test fault data for each camera. i Construct a camera scene-specific self-test fault dataset TGJ = {TCF1, TCF2, ..., TCF} i ,…,TCF n}, where TGJ is a self-test fault dataset for camera functions in a specific scene, and TCF is... i Given a subset of scene-specific function self-test fault data for the i-th camera, where n is the number of cameras, construct a combined scene-specific function self-test fault dataset based on the TGJ dataset.

[0122] Where TZH is the dataset of camera-specific scene function self-test fault combinations, n is the number of cameras in the substation, iC is the number of camera-specific scene function self-test fault combinations for the i-th camera, icY is the number of specific scene function self-test fault data for the c-th camera-specific scene function self-test fault combination of the i-th camera, and gs icy The self-test fault data for the y-th specific scene function of the c-th camera is a combination of self-test faults for the c-th camera in a specific scene function.

[0123] Based on the camera basic self-test fault combination dataset JZH and the camera specific scene function self-test fault combination dataset TZH, a camera fault level generation algorithm formula is used. Obtain the fault level of different fault combinations for each camera, where GD itc The fault level of the i-th camera is determined by combining the basic self-test fault combination of the t-th camera with the scene-specific function self-test fault combination of the c-th camera of the i-th camera. α1 is the weighting coefficient of the basic self-test faults of the cameras, tF is the number of basic self-test fault data in the basic self-test fault combination of the t-th camera, and β is the fault level of the t-th camera. tf Let ys be the influence weighting coefficient of the f-th basic self-test fault in the t-th basic self-test fault combination of the camera. tf Let jg be the impact value of the f-th basic self-test fault in the t-th camera basic self-test fault combination. tf Let f be the basic self-test fault data of the t-th camera basic self-test fault combination, α2 be the weight coefficient of the camera specific scene function self-test fault, icY be the number of specific scene function self-test fault data of the c-th camera specific scene function self-test fault combination, and δ be the number of specific scene function self-test fault data of the i-th camera. icy tg represents the influence weighting coefficient of the y-th scene-specific function self-test fault in the combination of scene-specific function self-test faults of the c-th camera and the i-th camera.icy Let gs be the impact value of the y-th scene-specific function self-test fault in the combination of scene-specific function self-test faults of the c-th camera and the i-th camera. icy This refers to the y-th scene-specific function self-test fault data of the c-th camera's scene-specific function self-test fault combination for the i-th camera. The fault level GD of the i-th camera is determined by combining the basic self-test fault combination of the t-th camera with the c-th camera's scene-specific function self-test fault combination. itc A camera fault level information dataset is constructed. Based on the camera basic self-test fault data subset BFS, the camera scene-specific functional self-test fault dataset TGJ, and the camera fault level information dataset, a camera fault level identification sub-model is built using machine learning techniques.

[0124] The sub-model integration sub-module integrates the camera basic self-test sub-model, the camera specific scene function self-test sub-model, and the fault level identification sub-model to build a camera self-test model.

[0125] Specifically, a camera self-test model is constructed by integrating the basic self-test sub-model of the camera, the self-test sub-model of the camera's specific scene functions, and the fault level identification sub-model through model fusion technology.

[0126] The camera self-test report acquisition module 24 collects camera self-test data information at regular intervals through the built-in information acquisition sensor in the camera according to the substation camera self-test element set and inputs it into the camera self-test model to generate a camera self-test report.

[0127] Furthermore, the camera self-test report acquisition module 24 includes the following sub-modules:

[0128] The camera self-test information acquisition submodule generates a camera self-test information dataset by periodically collecting camera self-test data information through the built-in information acquisition sensor in the camera based on the substation camera self-test element set.

[0129] Specifically, the substation's operation and maintenance personnel set the camera's self-test time according to the self-test requirements, and periodically collect the camera's self-test information data through the built-in information acquisition sensor in the camera based on the camera's self-test time and the substation's camera self-test element set.

[0130] The camera self-test report generation submodule inputs the camera self-test dataset into the camera self-test model for data analysis and generates a camera self-test report.

[0131] Specifically, the camera self-inspection report includes, but is not limited to, the number of each camera in the substation, the basic self-inspection qualified elements and their self-inspection data information, the specific scenario function self-inspection qualified elements and their self-inspection data information, the basic self-inspection fault elements and their fault data information, the specific scenario function self-inspection fault elements and their fault data information, and the fault level.

[0132] The camera fault alarm module 25 obtains the fault level of the camera based on the camera self-test report, obtains the substation camera fusion fault level through the fault level fusion algorithm, and selects an alarm method to trigger an alarm.

[0133] Furthermore, the camera fault alarm module 25 includes the following sub-modules:

[0134] The substation camera fault level fusion submodule obtains the fault level of the camera based on the camera self-test report and obtains the fused fault level of the substation camera through a fault level fusion algorithm.

[0135] Specifically, through fault level fusion algorithm Obtain the fault level of the substation camera fusion, where GRH is the fault level of the substation camera fusion, n is the number of faulty cameras in the substation, and λ is the number of faulty cameras. i Let GD be the fault impact weighting coefficient for the i-th faulty camera. i Let be the fault level of the i-th faulty camera.

[0136] The fault alarm submodule selects the alarm method and sends an alarm based on the fault level fused from the substation cameras.

[0137] Specifically, the alarm methods in the substation include, but are not limited to, telephone alarm, SMS alarm, email alarm, indicator light alarm, and voice alarm. The alarm method that matches the fault level of the substation camera is selected based on the fault level of the substation camera, so as to accurately notify the corresponding maintenance personnel to repair the camera.

[0138] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A self-testing method for cameras in substation scenarios, characterized in that, include: Step S1: The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number of cameras deployed and the deployment area of ​​the substation based on the camera configuration information, names the camera deployment area with area code, and numbers the cameras based on the number of cameras deployed and the area code of the substation, generating a camera number configuration information set. Step S2: The self-inspection center extracts the self-inspection elements of the substation cameras according to the camera number configuration information set, generates the substation camera self-inspection element set, obtains the substation industry monitoring standard documents and the historical monitoring operation data of the substation cameras through network communication technology, analyzes them to generate customized substation camera operation requirements, and generates a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set. Step S3: The self-inspection center generates a basic self-inspection classification dataset and a scene-specific functional self-inspection classification dataset for cameras based on the historical monitoring and operation data of substation cameras, the self-inspection element set of substation cameras, and the self-inspection standard set of substation cameras. It then constructs a basic self-inspection sub-model based on the basic self-inspection classification dataset and a scene-specific functional self-inspection sub-model based on the scene-specific functional self-inspection classification dataset. Finally, it generates a basic self-inspection fault combination dataset and a scene-specific functional self-inspection fault combination dataset based on the basic and scene-specific self-inspection classification datasets. A camera fault level information dataset is generated using a camera fault level generation algorithm. A camera fault level identification sub-model is constructed based on the camera fault level information dataset. Finally, a camera self-inspection model is constructed based on the basic self-inspection sub-model, the scene-specific functional self-inspection sub-model, and the camera fault level identification sub-model, as shown in the following sub-steps: Step S31: The self-inspection center constructs a basic self-inspection sub-model for the camera based on the historical monitoring and operation data of the substation camera, the self-inspection element set of the substation camera, and the self-inspection standard set of the substation camera. Step S32: The self-inspection center constructs a self-inspection sub-model for specific scene functions of the camera based on the historical monitoring and operation data of the substation camera, the self-inspection element set of the substation camera, and the self-inspection standard set of the substation camera. Step S33: The self-inspection center generates a camera fault level information dataset based on the camera basic self-inspection division dataset and the camera specific scene function self-inspection division dataset through the camera fault level generation algorithm, and constructs a camera fault level identification sub-model based on the camera fault level information dataset. Specifically, based on the camera's basic self-test data set, a subset of camera basic self-test fault data is obtained, and a combined dataset of camera basic self-test faults is constructed. Based on the self-test data of camera functions in specific scenes, a subset of self-test fault data for each camera in specific scenes is obtained, and a dataset of self-test fault data for camera functions in specific scenes is constructed. Based on the dataset of self-test fault data for camera functions in specific scenes, a combined dataset of self-test fault data for camera functions in specific scenes is constructed. Based on camera-based self-test fault combination dataset Combined dataset of self-test faults for camera-specific scene functions The algorithm formula for generating camera fault levels Obtain the fault level of different fault combinations for each camera, where, For the first The basic self-test fault combination of the cameras and the first The first camera The combination of self-test faults for specific scene functions of each camera is the first The fault level of each camera. The weighting coefficients for basic self-test faults of the camera. For the first The number of basic self-test fault data in each camera basic self-test fault combination. For the first The first combination of basic self-test faults of the camera The impact weighting coefficients of each basic self-test fault For the first The first combination of basic self-test faults of the camera The impact value of a basic self-test fault. For the first The first combination of basic self-test faults of the camera Basic self-test fault data, Weighting coefficients for camera self-test faults in specific scenarios. For the first The first camera The number of self-test fault data points for specific scene functions of a camera in a specific scene function combination. For the first The first camera The first combination of self-test faults for specific scene functions of a camera The impact weighting coefficient of self-test failure in a specific scenario For the first The first camera The first combination of self-test faults for specific scene functions of a camera The impact value of a self-test failure in a specific scenario. For the first The first camera The first combination of self-test faults for specific scene functions of a camera Self-test fault data for specific scenarios; based on the... The basic self-test fault combination of the cameras and the first The first camera The combination of self-test faults for specific scene functions of each camera is the first Fault level of each camera A camera fault level information dataset is constructed. Based on a subset of basic self-test fault data of cameras, a dataset of self-test fault data of camera functions in specific scenarios, and a dataset of camera fault level information, a camera fault level identification sub-model is constructed using machine learning techniques. Step S34: The self-test center integrates the camera basic self-test sub-model, the camera specific scene function self-test sub-model, and the fault level identification sub-model to construct the camera self-test model. Step S4: The self-inspection center collects camera self-inspection data information at regular intervals through the built-in information acquisition sensor in the camera based on the self-inspection element set of the substation camera and inputs it into the camera self-inspection model to generate a camera self-inspection report. Step S5: The self-inspection center obtains the fault level of the camera based on the camera self-inspection report, obtains the substation camera fusion fault level through the fault level fusion algorithm, and selects an alarm method to trigger an alarm.

2. The self-testing method for cameras in substation scenarios as described in claim 1, characterized in that, The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number and deployment area of ​​the cameras in the substation based on the camera configuration information, and names the camera deployment area with area code. It then numbers the cameras based on the number of cameras deployed in the substation and the area code, generating a camera number configuration information set, including the following sub-steps: Step S11: The self-inspection center obtains the camera configuration information of the substation based on the substation camera deployment information and generates a camera configuration information set. Step S12: The self-inspection center obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information set, assigns numbers to the cameras, and generates a camera number configuration information set.

3. The self-testing method for cameras in substation scenarios as described in claim 1, characterized in that, The self-inspection center extracts self-inspection elements from substation cameras based on the camera number configuration information set, generates a substation camera self-inspection element set, and obtains and analyzes industry monitoring standard documents and historical monitoring operation data of substation cameras through network communication technology to generate customized substation camera operation requirements. Based on the customized substation camera operation requirements and the substation camera self-inspection element set, a substation camera self-inspection standard set is generated, including the following sub-steps: Step S21: The self-inspection center generates a substation camera self-inspection element set based on the camera number configuration information set; Step S22: The self-inspection center uses network communication technology to obtain substation industry monitoring standard documents and historical monitoring operation data of substation cameras, analyzes them, and generates customized substation camera operation requirements. Step S23: The self-inspection center generates a set of self-inspection standards for substation cameras based on the established substation camera operation requirements and the set of self-inspection elements for substation cameras.

4. The self-testing method for cameras in substation scenarios as described in claim 1, characterized in that, The self-inspection center obtains the fault level of the camera based on the camera's self-inspection report, obtains the substation camera's fused fault level through a fault level fusion algorithm, and selects an alarm method to trigger an alarm, including the following sub-steps: Step S51: The self-inspection center obtains the fault level of the camera based on the camera self-inspection report and obtains the substation camera fusion fault level through the fault level fusion algorithm; Step S52: The self-test center selects the alarm method and issues an alarm based on the fault level of the substation camera fusion.

5. A camera self-testing system for substation scenarios, characterized in that, include: The camera configuration information acquisition module obtains the camera configuration information of the substation based on the substation camera deployment information, obtains the number of cameras deployed and the deployment area of ​​the substation based on the camera configuration information, names the camera deployment area with area code, and assigns a number to the camera based on the number of cameras deployed and the area code of the substation, generating a set of camera number configuration information. The camera self-inspection element and self-inspection standard generation module extracts substation camera self-inspection elements based on the camera number configuration information set, generates a substation camera self-inspection element set, obtains substation industry monitoring standard documents and historical monitoring operation data of substation cameras through network communication technology, analyzes and generates customized substation camera operation requirements, and generates a substation camera self-inspection standard set based on the customized substation camera operation requirements and the substation camera self-inspection element set. The camera self-test model construction module generates a basic self-test classification dataset and a scene-specific functional self-test classification dataset based on historical monitoring and operation data of substation cameras, substation camera self-test element sets, and substation camera self-test standard sets. It then constructs a basic self-test sub-model based on the basic self-test classification dataset, a scene-specific functional self-test sub-model based on the scene-specific functional self-test classification dataset, and generates a basic self-test fault combination dataset and a scene-specific functional self-test fault combination dataset based on the basic and scene-specific self-test classification datasets. Finally, it generates a camera fault level information dataset using a camera fault level generation algorithm and constructs a camera fault level identification sub-model based on this dataset. The module then builds the camera self-test model based on the basic self-test sub-model, the scene-specific functional self-test sub-model, and the fault level identification sub-model. This module includes the following sub-modules: The camera basic self-inspection sub-module constructs a camera basic self-inspection sub-model based on the historical monitoring and operation data of substation cameras, the substation camera self-inspection element set, and the substation camera self-inspection standard set. The sub-module for constructing a self-test sub-model for camera specific scene functions constructs a self-test sub-model for camera specific scene functions based on the historical monitoring and operation data of substation cameras, the self-test element set of substation cameras, and the self-test standard set of substation cameras. The camera fault level identification sub-model construction module generates a camera fault level information dataset based on the camera basic self-inspection dataset and the camera specific scene function self-inspection dataset, and constructs a camera fault level identification sub-model based on the camera fault level information dataset. Specifically, based on the camera's basic self-test data set, a subset of camera basic self-test fault data is obtained, and a combined dataset of camera basic self-test faults is constructed. Based on the self-test data of camera functions in specific scenes, a subset of self-test fault data for each camera in specific scenes is obtained, and a dataset of self-test fault data for camera functions in specific scenes is constructed. Based on the dataset of self-test fault data for camera functions in specific scenes, a combined dataset of self-test fault data for camera functions in specific scenes is constructed. Based on camera-based self-test fault combination dataset Combined dataset of self-test faults for camera-specific scene functions The algorithm formula for generating camera fault levels Obtain the fault level of different fault combinations for each camera, where, For the first The basic self-test fault combination of the cameras and the first The first camera The combination of self-test faults for specific scene functions of each camera is the first The fault level of each camera. The weighting coefficients for basic self-test faults of the camera. For the first The number of basic self-test fault data in each camera basic self-test fault combination. For the first The first combination of basic self-test faults of the camera The impact weighting coefficients of each basic self-test fault For the first The first combination of basic self-test faults of the camera The impact value of a basic self-test fault. For the first The first combination of basic self-test faults of the camera Basic self-test fault data, Weighting coefficients for camera self-test faults in specific scenarios. For the first The first camera The number of self-test fault data points for specific scene functions of a camera in a specific scene function combination. For the first The first camera The first combination of self-test faults for specific scene functions of a camera The impact weighting coefficient of self-test failure in a specific scenario For the first The first camera The first combination of self-test faults for specific scene functions of a camera The impact value of a self-test failure in a specific scenario. For the first The first camera The first combination of self-test faults for specific scene functions of a camera Self-test fault data for specific scenarios; based on the... The basic self-test fault combination of the cameras and the first The first camera The combination of self-test faults for specific scene functions of each camera is the first Fault level of each camera A camera fault level information dataset is constructed. Based on a subset of basic self-test fault data of cameras, a dataset of self-test fault data of camera functions in specific scenarios, and a dataset of camera fault level information, a camera fault level identification sub-model is constructed using machine learning techniques. The sub-model integration sub-module integrates the camera basic self-test sub-model, the camera specific scene function self-test sub-model, and the fault level identification sub-model to build a camera self-test model. The camera self-test report acquisition module collects camera self-test data information at regular intervals through the built-in information acquisition sensor in the camera based on the substation camera self-test element set and inputs it into the camera self-test model to generate a camera self-test report. The camera fault alarm module obtains the fault level of the camera based on the camera self-test report, obtains the substation camera fusion fault level through a fault level fusion algorithm, and selects an alarm method to trigger an alarm.

6. A camera self-testing system for substation scenarios as described in claim 5, characterized in that, The camera configuration information acquisition module includes: The camera configuration information acquisition submodule acquires the camera configuration information of the substation based on the substation camera deployment information and generates a camera configuration information set. The camera numbering submodule obtains the number and deployment area of ​​cameras in the substation based on the camera configuration information set, assigns numbers to the cameras, and generates a camera numbering configuration information set.

7. A camera self-testing system for substation scenarios as described in claim 5, characterized in that, The camera self-inspection element self-inspection standard generation module specifically includes: The camera self-inspection element acquisition submodule generates a substation camera self-inspection element set based on the camera number and configuration information set. The camera operation requirements acquisition submodule uses network communication technology to acquire industry monitoring standard documents for substations and historical monitoring operation data of substation cameras, analyzes them, and generates customized substation camera operation requirements. The camera self-inspection standard generation submodule generates a set of self-inspection standards for substation cameras based on the established substation camera operation requirements and the set of self-inspection elements for substation cameras.

8. A camera self-testing system for substation scenarios as described in claim 5, characterized in that, The camera fault alarm module specifically includes: The substation camera fault level fusion submodule obtains the fault level of the camera based on the camera self-test report and obtains the fused fault level of the substation camera through a fault level fusion algorithm. The fault alarm submodule selects the alarm method based on the fault level fused from the substation cameras.

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